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DATA INTEGRATION TECHNOLOGIES
have experienced explosive growth in the last few
years, and data warehousing has played a major role in
the integration process. A data warehouse is asubjectoriented,
integrated, time-variant, and nonvolatile
collection of data that supports managerial decision
making [4]. Data warehousing has been cited as the
highest-priority post-millennium project ofmore
than half of IT executives. A large number of data
warehousing methodologies and tools are available to
support the growing market. However, with so many
methodologies to choose from, a majorconcern for
many firms is which one to employ in a given data
warehousing project. In this article, we review and
compare several prominent data warehousing
methodologies based on a common setof attributes.
Online transaction processing (OLTP) systems are
useful for addressing the operational data needs of a
firm. However, they are not well suited for supporting
decision-supportqueries or business questions that
managers typically need to address. Such questions
involve analytics including aggregation, drilldown,
and slicing/dicing of data, which are best supported
by onlineanalytical processing (OLAP) systems.
Data warehouses support OLAP applications by
storing and maintaining data in multidimensional
format. Data in an OLAP warehouse is extracted and
loaded frommultiple OLTP data sources (including
DB2, Oracle, IMS databases, and flat files) using
Extract, Transfer, and Load (ETL) tools.
The warehouse is located in a presentation server.
It can spanenterprisewide data needs or can be a collection
of “conforming” data marts [8]. Data marts
(subsets of data warehouses) are conformed by following
a standard set of attribute declarations called adata warehouse bus. The data warehouse uses a metadata
repository to integrate all of its components. The
metadata stores definitions of the source data, data
models for target databases, and...